2009Haiyang kexueRequires access

Morphological variations analysis of five different populations of Scapharca subcrenata in China

Ying-Zhu Rao

Open publisher page 1 citations

Abstract

Based on 10 morphological characters of populations of Scapharca subcrenata ,from Shandong, Tianjin, Guangdong, Hainan and Guangxi, multivariate morphometrics were used to investigate their morphological variations among the five different geographical populations. The results of cluster analysis and principal component analysis showed that the populations of Scapharca subcrenata form Tianjin Tanggu and Shandong Qingdao were rather similar in morphology, whereas Guangxi Beihai population different form other populations in morphology. The principal component analysis resulted in three principal components. The contributory ratios of the three principal components were 34.70 %, 19.80 % and 15.00 % respectively, and the cumulative contributory ratio was 69.50 %. The result of stepwise discriminant analysis revealed that the five populations differed significantly in morphology (P0.01). The discriminant functions of five populations were established, and the discriminant accuracy was 45.45 %~95.45 % for P1 and 36.36 %~95.45 % for P2. The average discriminant accuracy was 74.50 %.

About this research paper

What this paper is about

Based on 10 morphological characters of populations of Scapharca subcrenata ,from Shandong, Tianjin, Guangdong, Hainan and Guangxi, multivariate morphometrics were used to investigate their morphological variations among the five different geographical populations. The results of cluster analysis and principal component analysis showed that the populations of Scapharca subcrenata form Tianjin Tanggu and Shandong Qingdao were rather similar in morphology, whereas Guangxi Beihai population different form other populations in morphology. The principal component analysis resulted in three principal components. The contributory ratios of the three principal components were 34.70 %, 19.80 % and 15.00 % respectively, and the cumulative contributory ratio was 69.50 %. The result of stepwise discriminant analysis revealed that the five populations differed significantly in morphology (P0.01). The discriminant functions of five populations were established, and the discriminant accuracy was 45.45 %~95.45 % for P1 and 36.36 %~95.45 % for P2. The average discriminant accuracy was 74.50 %.

Why it matters

OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Based on 10 morphological characters of populations of Scapharca subcrenata ,from Shandong, Tianjin, Guangdong, Hainan and Guangxi, multivariate morphometrics were used to investigate their morphological variations among the five different geographical populations. The results of cluster analysis and principal component analysis showed that the populations of Scapharca subcrenata form Tianjin Tanggu and Shandong Qingdao were rather similar in morphology, whereas Guangxi Beihai population different form other populations in morphology. The principal component analysis resulted in three principal components. The contributory ratios of the three principal components were 34.70 %, 19.80 % and 15.00 % respectively, and the cumulative contributory ratio was 69.50 %. The result of stepwise discriminant analysis revealed that the five populations differed significantly in morphology (P0.01). The discriminant functions of five populations were established, and the discriminant accuracy was 45.45 %~95.45 % for P1 and 36.36 %~95.45 % for P2. The average discriminant accuracy was 74.50 %.

Key concepts: Principal component analysis, Morphometrics, Biology, Morphological analysis, Linear discriminant analysis, Morphology (biology), Population, Multivariate statistics

Related papers

Back to paper searchBrowse research topicsOriginal source
Morphological variations analysis of five different populations of Scapharca subcrenata in China — Research Paper | ScholarLens